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Introduction to R: A Language for Statistical Computing and Graphics

This tutorial, presented by Eric Gilliland and Matt Pocernich on March 29, 2006, provides a comprehensive overview of R, an open-source programming language designed for statistical computing and graphics. R was influenced by Becker, Chambers & Wilks' S and Sussman's Scheme. Developed by Ross Ihaka and Robert Gentleman, R has grown significantly, offering over 600 packages covering diverse topics from Bayesian Inference to Machine Learning. This guide highlights R’s community, its open-source nature, and the extensive resources available for users across various platforms.

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Introduction to R: A Language for Statistical Computing and Graphics

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  1. A tutorial by Eric Gilliland and Matt Pocernich March 29th, 2006 Introduction toa language and environment for statistical computing and graphics.

  2. Origins of R • Influenced by two existing languages: • Becker, Chambers & Wilks' S (ATT) • Sussman's Scheme • Initially written by Ross Ihaka and Robert Gentleman • R-0.49 23-Apr-1997 05:53 959k • R-latest 20-Dec-2005 02:35 13.0M

  3. R is Open Source ! • GNU General Public License (GPL) • The license must not restrict anyone from making use of the program in a specific field of endeavor. For example, it may not restrict the program from being used in a business, or from being used for genetic research. • All code is visible

  4. The R Community • Developers • R Core Group (17 members, all -male?), only 2 have left since 1997 • Major update in April/October (freeze dates, beta versions, bug tracking, ...) • Mailing lists • Help list ~ 90 messages/day, archived, searchable. • Packages – more than 600 packages (11/2005)

  5. Packages • More than 600 packages • Nearly every conceivable topic • Hard to search • Contributors include a who's who in statistics • Task page • Bayesian Bayesian Inference • Cluster Cluster Analysis & Finite Mixture Models • Econometrics Computational Econometrics • Environmetrics Analysis of Ecological and Environmental Data • Finance Empirical Finance • Genetics Statistical Genetics • gR gRaphical models in R • MachineLearning Machine Learning & Statistical Learning • Multivariate Multivariate Statistics • SocialSciences Statistics for the Social Sciences • Spatial Analysis of Spatial Data

  6. r-project.org • Contains everything • Source code • Documentation • Newsletter • Mailing list • Packages

  7. Miscelleneous • Works on most platforms • Mac, Windows, Linux, Solaris, ... • Should be used with an editor • Windows version contains notebook • Xemacs • Winedt • Works in BATCH Mode • Rweb • Ideal for use with cvs !!!

  8. R Culture • Try fortunes package

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